A fundamental step in image interpretation is edge detection. The goal of edge detection is to identify the pixels in the image that correspond to the edges of objects perceived. Here, we use the term image to include more general data sets. An example of an image 1 having an edge 2 is shown on FIG. 1. The image may be from any source, including but not limited to a medical ultrasound imaging device, a light-based imaging device such as Optical Coherence Tomography (OCT) and Optical Coherence Domain Reflectography (OCDR) imaging devices, and magnetic resonance imaging (MRI).
A common approach to edge detection is to find pixels which locally maximize the gradient magnitude in the gradient direction. Standard edge detectors known in the art, such as the Sobel and Prewitt detectors, are finite-difference-based first-derivative operators that pick up high-frequency responses at image edges. Another detector known in the art is the Canny detector, which formulates edge detection as an optimization problem. These and related techniques have been used with varying success. However, there remains the unsolved problem of quality edge detection with many real-world images in which edges are more appropriately characterized by textural differences rather than differences in intensity. One example of these types of edges is an edge defined in a physiological image, such as an ultrasound image.
In such images, edges are not necessarily associated with sharp changes in image values. This lack of association stems at least from three causes. First, objects may not have a strong contrast with their backgrounds. Second, objects are often covered with texture or markings that generate edges of their own, so much so that it is often impractical or impossible to sort out the relevant pieces of the object boundary. Finally, artifacts such as shadows may generate edges that have no relation to object boundaries.
The inadequacy of traditional edge detection methods when applied to these types of images is well known. Accordingly, an improved edge detection method would be desirable.